title: EEG epileptic seizures separation with multivariate empirical mode decomposition for diagnostic purposes.
abstract: We present a successful application of a soft computing approach based on the multivariate empirical mode decomposition (MEMD) method to EEG epileptic seizures separation. The results of the automatic multivatiate intrinsic mode functions (IMF) clustering allowed us to separate the seizure related spikes and sharp waves. The results of the proposed method have been compared with classical blind separation approach based on ICA, which failed to identify the non-linear and non-stationary signals related to the brain seizures. The proposed method supports epileptic seizure diagnostic methods.
authors.forename: Tomasz M
authors.initials: TM
authors.lastname: Rutkowski
authors.forename: Zbigniew R
authors.initials: ZR
authors.lastname: Struzik
authors.forename: Danilo P
authors.initials: DP
authors.lastname: Mandic
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003943:Diagnostic Techniques, Neurological / Q000295:instrumentation*; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D009799:Ocular Physiological Phenomena